class_docstr: >>> LitClassifier(Backbone())  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  LitClassifier(
    (backbone): ...
  )
code: def configure_optimizers(self):
      # self.hparams available because we called self.save_hyperparameters()
      return torch.optim.Adam(
          self.parameters(), lr=self.hparams.learning_rate
      )
  
imports: from os import path
  from typing import Optional
  import torch
  from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.demos.mnist_datamodule import MNIST
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
label_desc: Use image node_id 7 for calling the LitClassifier obj's underlying member method code with example usage: obj.configure_optimizers() and returns: torch